Sparse, mean reverting portfolio selection using simulated annealing

Norbert Fogarasi () and Janos Levendovszky ()
Additional contact information
Norbert Fogarasi: Department of Networked Systems and Services, Budapest University of Technology and Economics, Postal: Department of Networked Systems and Services, Budapest University of Technology and Economics,, Budapest, Hungary
Janos Levendovszky: Department of Networked Systems and Services, Budapest University of Technology and Economics, Postal: Department of Networked Systems and Services, Budapest University of Technology and Economics,, Budapest, Hungary

Algorithmic Finance, 2013, vol. 2, issue 3-4, 197-211

Abstract: We study the problem of finding sparse, mean reverting portfolios based on multivariate historical time series. After mapping the optimal portfolio selection problem into a generalized eigenvalue problem, we propose a new optimization approach based on the use of simulated annealing. This new method ensures that the cardinality constraint is automatically satisfied in each step of the optimization by embedding the constraint into the iterative neighbor selection function. We empirically demonstrate that the method produces better mean reversion coefficients than other heuristic methods, but also show that this does not necessarily result in higher profits during convergence trading. This implies that more complex objective functions should be developed for the problem, which can also be optimized under cardinality constraints using the proposed approach.

Keywords: mean reversion; convergence trading; parameter estimation; stochastic optimization; simulated annealing (search for similar items in EconPapers)
JEL-codes: C00 C10 D40 (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations: View citations in EconPapers (4)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ris:iosalg:0013

Access Statistics for this article

Algorithmic Finance is currently edited by Phil Maymin

More articles in Algorithmic Finance from IOS Press
Bibliographic data for series maintained by Saskia van Wijngaarden ().